본문 바로가기 주메뉴 바로가기
22대 대한민국 국회 국회정보길라잡이 국회의원검색
회원가입 로그인
HOME

정보검색

소장정보 검색

국회도서관 홈으로 정보검색 소장정보 검색
결과 내 검색 동의어 포함
결과 내 검색 동의어 포함

-

목차보기

Contents

Pedestrian collision avoidance using deep reinforcement learning / Alireza Rafiei ; Amirhossein Oliaei Fasakhodi ; Farshid Hajati 1

ABSTRACT 1

NOMENCLATURE 1

1. INTRODUCTION 1

1.1. Related Works 2

2. MATERIALS AND METHODS 2

2.1. RL and DQN 2

2.2. Pedestrian Collision Avoidance Approach 3

2.3. Risk Degree 5

3. EXPERIMENTAL RESULTS 6

4. DISCUSSION 8

5. CONCLUSION 8

REFERENCES 9

초록보기

The use of intelligent systems to prevent accidents and safety enhancement in vehicles is becoming a requirement. Besides, the development of autonomous cars is progressing every day. One of the main challenges in transportation is the high mortality rate of vehicles colliding with pedestrians. This issue becomes severe due to various and abnormal situations. This paper proposes a new intelligent algorithm for pedestrian collision avoidance based on deep reinforcement learning. A deep Q-network (DQN) is designed to discover an optimal driving policy for pedestrian collision avoidance in diverse environments and conditions. The algorithm interacts with the vehicle and the pedestrian agents and uses a specific reward function to train the model. We have used Car Learning to Act (CARLA), an open-source autonomous driving simulator, for training and verifying the model in various conditions. Applying the proposed algorithm to a simulated environment reduces vehicles and pedestrians’ collision by about 64 %, depending on the environment. Our findings offer an early-warning solution to mitigate the risk of a crash of vehicles and pedestrians in the real world.

권호기사

권호기사 목록 테이블로 기사명, 저자명, 페이지, 원문, 기사목차 순으로 되어있습니다.
기사명 저자명 페이지 원문 목차
Performance prediction of engine coolant circulation type melting system of frozen urea : effect of heating coil design Seokhoon Jeong, Hyunjun Kim, Ohyun Kwon, Eunyong Park, Jeongho Kang p. 583-589

보기
Multi-stage ignition system with self-adaptive control for lean combustion in SI natural gas engine Hui Song Tang, Chang Shui Wu p. 591-601

보기
Numerical study on the design of air cooled battery thermal management system for eco-friendly vehicles Alemayehu Wakjira Huluka, Chul-Ho Kim p. 603-612

보기
Pedestrian collision avoidance using deep reinforcement learning Alireza Rafiei, Amirhossein Oliaei Fasakhodi, Farshid Hajati p. 613-622

보기
Analysis and optimization of beam opening angle of torsion beam suspension to improve frequency response characteristics of whole vehicle Jin Gao, Peifeng Han p. 623-640

보기
Spray development process utilizing a multi-hole GDI injector with different spray hole lengths and step hole diameters Jeonghwan Park, Sungwook Park p. 641-649

보기
High-load expansion by varying effective compression ratio using variable valve duration system under dual-fuel premixed compression ignition Kihong Kim, Donghyun Lim, Hyungjin Shin, Sanghyun Chu, Jeongwoo Lee, Kyoungdoug Min p. 651-658

보기
Impact overlaps on occupant lower limb injuries under car frontal crash Sen Xiao, Xuewei Shi, Xiuxiu Sun, Hao Zhang, Weijie Ma, Zhixin Liu p. 659-665

보기
Regenerative braking control strategies with fixed ratio and variable ratio braking forces optimization distribution for electric vehicles during downhill process Li Xiangjie, Zhang Xiangwen, Wang Yangxiong p. 667-681

보기
Valve optimization with system-fluid-magnetic co-simulation and design of experiments U Oh, Norihiko Nonaka, Jun Ishimoto p. 683-692

보기
Monocular vision slam research for parking environment with low light Sumin Zhang, Yongshuai Zhi, Shouyi Lu, Ze Lin, Rui He p. 693-703

보기
Spray collapse in a side-mount gasoline direct injection injector with various injection conditions and injector nozzle configurations Huijun Kim, Seungho Yang, Sungwook Park p. 705-715

보기
Influencing factors of electric vehicle economy based on continuously variable transmission Bing Fu, Taiping Zhu, Jingang Liu, Youhong Zhao, Jianwen Chen p. 717-728

보기
Sideslip angle control of electronic-four-wheel drive vehicle using backstepping controller Giseo Park p. 729-739

보기
Recent developments and trends in flexible forming technology Hyungrim Lee, Namsu Park, Minki Kim, Myoung-Gyu Lee, Jung Han Song p. 741-763

보기
Road torque modeling for electric power steering systems Byeonggwan Jang, Dongwook Lee, Kyuwon Kim, Kyung-Soo Kim p. 765-773

보기
Effect of hydrocarbon concentration on particulate deposition and microstructure of the deposit in exhaust gas recirculation cooler Jinhui Li, Xun Zhang, Huan Wu, Xueshun Wu, Zhiqiang Han, Wei Tian p. 775-784

보기
Control strategy based on flow conservation equation for high-pressure common rail system Zi-Guang Gao, Hong-Meng Li, Chun-Long Xu, Guo-Xiu Li, Min Wang p. 793-803

보기
Regenerative braking control strategy based on multi-source information fusion under environment perception Yue Shang, Chao Ma, Kun Yang, Di Tan p. 805-815

보기
Simulator study on the response time and defensive behavior of drivers in a cut-in situation Myeongkyu Lee, Songhui Kim, Jonghyuk Kim, Ji Hyun Yang p. 817-827

보기
Personalized speed planning algorithm using a statistical driver model in car-following situations Seung Eon Baek, Hak Su Kim, Manbae Han p. 829-840

보기
Disturbance observer based active independent front steering control for improving vehicle yaw stability and tire utilization Murat Gözü, Basar Ozkan, Mümin Tolga Emirler p. 841-854

보기
Computational study on the frictional power loss reduction of piston ring with laser surface texturing on the cylinder liner Siyoul Jang p. 855-865

보기
Comprehensive effects on performance and emission of GDI gasoline engine with electric supercharger and EGR Zhaoming Huang, Jianping Li, Kai Shen, Li Wang, Hao Pan, Weiguo Chen, Jinyuan Pan p. 867-873

보기
Development of a narrow passage driving system for semi-trailer truck by using MPC Sung Hwan Yun, Jung Gun Yang, Kun Soo Huh p. 785-791

보기

참고문헌 (47건) : 자료제공( 네이버학술정보 )

참고문헌 목록에 대한 테이블로 번호, 참고문헌, 국회도서관 소장유무로 구성되어 있습니다.
번호 참고문헌 국회도서관 소장유무
1 Anowar, S., Alam, M. D. and Raihan, M. A. (2008). Analysis of accident patterns at selected intersections of an urban arterial. Proc. 21st ICTCT Workshop, Melbourne, Australia. 미소장
2 Badue, C., Guidolini, R., Carneiro, R. V., Azevedo, P., Cardoso, V. B., Forechi, A., Jesus, L., Berriel, R., Paixão, T. M., Mutz, F., Veronese, L. d. P., Oliveira-Santos, T. and De Souza, A. F. (2020). Self-driving cars: A survey. Expert Systems with Applications, 165, 113816. 미소장
3 Belmonte, F. J., Martín, S., Sancristobal, E., Ruipérez-Valiente, J. A. and Castro, M. (2020). Overview of embedded systems to build reliable and safe ADAS and AD systems. IEEE Intelligent Transportation Systems Magazine (in press), 1–10. 미소장
4 Busoniu, L., Babuska, R. and De Schutter, B. (2008). A comprehensive survey of multiagent reinforcement learning. IEEE Trans. Systems, Man, and Cybernetics, Part C (Applications and Reviews) 38, 2, 156–172. 미소장
5 Chae, H., Kang, C. M., Kim, B., Kim, J., Chung, C. C. and Choi, J. W. (2017). Autonomous braking system via deep reinforcement learning. IEEE 20th Int. Conf. Intelligent Transportation Systems (ITSC), Yokohama, Japan. 미소장
6 De, S., Mukherjee, A. and Ullah, E. (2018). Convergence guarantees for RMSProp and ADAM in non-convex optimization and an empirical comparison to Nesterov acceleration. arXiv: 1807. 06766. 미소장
7 Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A. and Koltun, V. (2017). CARLA: An open urban driving simulator. Proc. 1st Annual Conf. Robot Learning, in PMLR, 78, 1–16. 미소장
8 Galvani, M. (2019). History and future of driver assistance. IEEE Instrumentation & Measurement Magazine 22, 1, 11–16. 미소장
9 Gelbal, S. Y., Guvenc, B. A. and Guvenc, L. (2020). Collision avoidance of low speed autonomous shuttles with pedestrians. Int. J. Automotive Technology 21, 4, 903–917. 미소장
10 Gu, S., Holly, E., Lillicrap, T. and Levine, S. (2017). Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. IEEE Int. Conf. Robotics and Automation (ICRA), Singapore. 미소장
11 Hacohen, S., Shoval, S. and Shvalb, N. (2020). The paradox of pedestrian’s risk aversion. Accident Analysis &Prevention, 142, 105518. 미소장
12 Hamid, U. Z. A., Zakuan, F. R. A., Zulkepli, K. A., Azmi, M. Z., Zamzuri, H., Rahman, M. A. A. and Zakaria, M. A. (2017). Autonomous emergency braking system with potential field risk assessment for frontal collision mitigation. IEEE Conf. Systems, Process and Control (ICSPC), Karunya Nagar, Coimbatore, India. 미소장
13 Hojjati-Emami, K., Dhillon, B. and Jenab, K. (2012). Reliability prediction for the vehicles equipped with advanced driver assistance systems (ADAS) and passive safety systems (PSS). Int. J. Industrial Engineering Computations 3, 5, 731–742. 미소장
14 Hussain, Q., Feng, H., Grzebieta, R., Brijs, T. and Olivier, J. (2019). The relationship between impact speed and the probability of pedestrian fatality during a vehiclepedestrian crash: A systematic review and meta-analysis. Accident Analysis & Prevention, 129, 241–249. 미소장
15 Huval, B., Wang, T., Tandon, S., Kiske, J., Song, W., Pazhayampallil, J., Andriluka, M., Rajpurkar, P., Migimatsu, T., Cheng-Yue, R., Mujica, F., Coates, A. and Ng, A. Y. (2015). An empirical evaluation of deep learning on highway driving. arXiv: 1504. 01716. 미소장
16 Kanarachos, S., Christopoulos, S. R. G. and Chroneos, A. (2018). Smartphones as an integrated platform for monitoring driver behaviour: The role of sensor fusion and connectivity. Transportation Research Part C:Emerging Technologies, 95, 867–882. 미소장
17 Koehler, F. and Risteski, A. (2018). Representational power of ReLU networks and polynomial kernels: Beyond worst-case analysis. arXiv: 1805. 11405. 미소장
18 Lankarani, K. B., Heydari, S. T., Aghabeigi, M. R., Moafian, G., Hoseinzadeh, A. and Vossoughi, M. (2014). The impact of environmental factors on traffic accidents in Iran. J. Injury and Violence Research 6, 2, 64–71. 미소장
19 Lee, D. and Yeo, H. (2016). Real-time rear-end collisionwarning system using a multilayer perceptron neural network. IEEE Trans. Intelligent Transportation Systems 17, 11, 3087–3097. 미소장
20 Li, D., Ranjitkar, P., Zhao, Y., Yi, H. and Rashidi, S. (2017a). Analyzing pedestrian crash injury severity under different weather conditions. Traffic Injury Prevention 18, 4, 427–430. 미소장
21 Li, J., Yao, L., Xu, X., Cheng, B. and Ren, J. (2020). Deep reinforcement learning for pedestrian collision avoidance and human-machine cooperative driving. Information Sciences, 532, 110–124. 미소장
22 Li, Z., Yu, Q., Zhao, X., Yu, M., Shi, P. and Yan, C. (2017b). Crashworthiness and lightweight optimization to applied multiple materials and foam-filled front end structure of auto-body. Advances in Mechanical Engineering 9, 8, 1–21. 미소장
23 Likmeta, A., Metelli, A. M., Tirinzoni, A., Giol, R., Restelli, M. and Romano, D. (2020). Combining reinforcement learning with rule-based controllers for transparent and general decision-making in autonomous driving. Robotics and Autonomous Systems, 131, 103568. 미소장
24 Lin, L., Zhang, D., Luo, P. and Zuo, W. (2020). Human Centric Visual Analysis with Deep Learning. Springer. Singapore. 미소장
25 Lucidi, F., Girelli, L., Chirico, A., Alivernini, F., Cozzolino, M., Violani, C. and Mallia, L. (2019). Personality traits and attitudes toward traffic safety predict risky behavior across young, adult, and older drivers. Frontiers in Psychology, 10, 536. 미소장
26 Malin, F., Norros, I. and Innamaa, S. (2019). Accident risk of road and weather conditions on different road types. Accident Analysis & Prevention, 122, 181–188. 미소장
27 McDonald, A. D., Lee, J. D., Schwarz, C. and Brown, T. L. (2018). A contextual and temporal algorithm for driver drowsiness detection. Accident Analysis & Prevention, 113, 25–37. 미소장
28 Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D. and Riedmiller, M. (2013). Playing atari with deep reinforcement learning. arXiv:1312. 5602. 미소장
29 Pinchon, N., Cassignol, O., Nicolas, A., Bernardin, F., Leduc, P., Tarel, J.-P., Brémond, R., Bercier, E. and Brunet, J. (2018). All-weather vision for automotive safety: Which spectral band?. Int. Forum on Advanced Microsystems for Automotive Applications. Springer. Cham, Switzerland. 미소장
30 Pop, D. O. (2019). Detection of pedestrian actions based on deep learning approach. Studia Universitatis Babeş-Bolyai. Informatica, Babeș-Bolyai University. 미소장
31 Powell, W. B. (2021). From reinforcement learning to optimal control: A unified framework for sequential decisions. Handbook of Reinforcement Learning and Control. Springer. Cham, Switzerland. 미소장
32 Ramachandran, P., Zoph, B. and Le, Q. V. (2017). Swish: A self-gated activation function. arXiv: 1710. 05941. 미소장
33 Roh, C. G., Kim, J. and Im, I. (2020). Analysis of impact of rain conditions on ADAS. Sensors 20, 23, 6720. 미소장
34 Rosén, E. and Sander, U. (2009). Pedestrian fatality risk as a function of car impact speed. Accident Analysis &Prevention 41, 3, 536–542. 미소장
35 Savchenko, V. V. and Litarovich, V. V. (2020). Classification of tablesemantically binary relevant information for drivers in highly automated vehicles. IOP Conf. Series:Materials Science and Engineering 819, 1, 012042. 미소장
36 Schaul, T., Quan, J., Antonoglou, I. and Silver, D. (2015). Prioritized experience replay. arXiv: 1511. 05952. 미소장
37 Schrum, K. D., De Albuquerque, F. D. B., Sicking, D. L., Falle, R. K. and Reid, J. D. (2014). Correlation between crash severity and embankment geometry. J. Transportation Safety & Security 6, 4, 321–334. 미소장
38 Sewalkar, P. and Seitz, J. (2019). Vehicle-to-pedestrian communication for vulnerable road users: Survey, design considerations, and challenges. Sensors 19, 2, 358. 미소장
39 Shaaban, K., Muley, D. and Mohammed, A. (2018). Analysis of illegal pedestrian crossing behavior on a major divided arterial road. Transportation Research Part F: Traffic Psychology and Behaviour, 54, 124–137. 미소장
40 Sombolestan, S. M., Rasooli, A. and Khodaygan, S. (2019). Optimal path-planning for mobile robots to find a hidden target in an unknown environment based on machine learning. J. Ambient Intelligence and Humanized Computing 10, 5, 1841–1850. 미소장
41 Tabata, T., Konet, H. and Kanuma, T. (2010). Development of Nissan approaching vehicle sound for pedestrians. EVS-25 Shenzhen, China 5, 9, 1–6. 미소장
42 Wang, P., Motamedi, S., Canas Bajo, T., Zhou, X., Zhang, T., Whitney, D. and Chan, C. Y. (2019a). Safety Implications of Automated Vehicles Providing External Communication to Pedestrians. Research Report No. UC-ITS-2019-12. 미소장
43 Wang, Y., He, H. and Sun, C. (2018). Learning to navigate through complex dynamic environment with modular deep reinforcement learning. IEEE Trans. Games 10, 4, 400–412. 미소장
44 Wang, Z., Wan, Q., Qin, Y., Fan, S. and Xiao, Z. (2019b). Intelligent algorithm in a smart wearable device for predicting and alerting in the danger of vehicle collision. J. Ambient Intelligence and Humanized Computing, 11, 3841–3852. 미소장
45 Wang, Z., Wan, Q., Qin, Y., Fan, S. and Xiao, Z. (2020). Research on intelligent algorithm for alerting vehicle impact based on multi-agent deep reinforcement learning. J. Ambient Intelligence and Humanized Computing 12, 1, 1337–1347. 미소장
46 Yin, S., Chen, H., Wu, Y., Li, Y. and Xu, J. (2018). Introducing composite lattice core sandwich structure as an alternative proposal for engine hood. Composite Structures, 201, 131–140. 미소장
47 Zai, A. and Brown, B. (2020). Deep Reinforcement Learning in Action. Manning Publications. Shelter Island, NY, USA. 미소장

추천서가 (다양한 추천 자료를 만나보세요)

권호기사보기

권호기사 목록 테이블로 기사명, 저자명, 페이지, 원문, 기사목차 순으로 되어있습니다.
기사명 저자명 페이지 원문 기사목차